Method, apparatus, electronic device, and storage medium for web search

ABSTRACT

The present disclosure relates to a method and an apparatus, an electronic device and a storage medium for web search. The method includes: acquiring a steady-state visual evoked potential in EEG information, where the steady-state visual evoked potential is generated when a user gazes at a key on a query inputting keyboard in a visual spelling page; sending the steady-state visual evoked potential to a server, such that the server, based on the steady-state visual evoked potential, determines a character string inputted by the user, and a landing page corresponding to the character string; and in response to receiving the landing page sent by the server, displaying the landing page.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based upon and claims the benefit of apriority of Chinese Patent Application No. 202111423899.4, filed on Nov.26, 2021, the entire contents of which are incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to the technical field of computers, andin particular, to a method, an apparatus, an electronic device and astorage medium for web search.

BACKGROUND

With the increasingly wide application of the Internet, search enginesare becoming a channel for many people to seek information, changing theway people live and think. In the related art, from the perspective ofusers, a search engine may provide a page containing a search box, intowhich users may enter a query term according to their search intent,which will then be submitted to the search engine through a browser.After that, the search engine will return an information list of searchresults related to the entered query term, among which users may clickto satisfy their information needs.

Driven by the diversified information needs and big data technology, thesearch technology is developing and improving continuously. However, inorder to better improve the search experience, the following aspects ofthe search technology are to be addressed.

Firstly, in a typical search in the related art, a user summarizes theinformation needs into a query term and submits it to the search engine.Usually, the query term constructed by the user may hardly convey theactual information needs of the user accurately since it may beambiguous or broad. However, the search engine may highly rely on thisquery term to retrieve and rank relevant documents, bringing uncertaintyand ambiguity to the search results, which further hinders theimprovement of search performance.

Secondly, although the search engine in the related art may collectimplicit feedbacks of users such as clicks, dwell time and the like,such implicit feedbacks may not objectively reflect actual feelings ofthe users, and may also bring more noise, resulting in negativeinterference on the ranking of the search results.

Thirdly, interaction theories and methods of search engines in therelated art are designed and constructed based on a mouse and akeyboard, which is not suitable for some special scenarios and users.

SUMMARY

The present disclosure provides a web search technical solution.

According to an aspect of the present disclosure, a web search methodapplied to a user terminal includes: displaying a visual spelling pagehaving a query keyboard; acquiring a steady-state visual evokedpotential from electroencephalogram (EEG) information, where thesteady-state visual evoked potential is generated when a user gazes at akey on the query keyboard; sending the steady-state visual evokedpotential to a server; and in response to receiving the landing pagesent by the server, displaying the landing page. For example, on theserver side, in response to the steady-state visual evoked potential,the server, based on the steady-state visual evoked potential,determines a character string inputted by the user and a the landingpage corresponding to the character string.

In a possible implementation, after displaying the landing page, themethod further includes: acquiring EEG information generated when theuser gazes at the landing page; sending the EEG information to theserver, such that the server detects the user's feedback information inresponse to the landing page based on the EEG information, anddetermines a search engine result page based on the feedbackinformation, where the feedback information includes emotion informationdetermined based on the EEG information, and the search engine resultpage includes at least two search results corresponding to the characterstring; and in response to receiving the search engine result page sentby the server, displaying the search engine result page.

In a possible implementation, the visual spelling page further includesa query suggestion module, and the sending the steady-state visualevoked potential to a server, such that the server determines, based onthe steady-state visual evoked potential, a character string inputted bythe user and a landing page corresponding to the character stringincludes: sending the steady-state visual evoked potential to theserver, such that the server, based on the steady-state visual evokedpotential, determines the character string inputted by the user and atleast one query term corresponding to the string; in response toreceiving the character string and the at least one query term sent bythe server, displaying the character string and the at least one queryterm through the query suggestion module; acquiring a search context inthe EEG information, where the search context includes user stateinformation; and sending the search context to the server, such that theserver determines the landing page based on the at least one query termand the search context.

In a possible implementation, the method further includes: acquiring eyemovement information, and the sending the steady-state visual evokedpotential to a server, such that the server determines, based on thesteady-state visual evoked potential, a character string inputted by theuser and a landing page corresponding to the string, includes: sendingthe steady-state visual evoked potential and the eye movementinformation to the server, such that the server determines the stringinputted by the user based on the steady-state visual evoked potentialand the eye movement information.

In a possible implementation, the method further includes: in a casewhere the eye movement information and/or the EEG information indicatespaying attention to a preset area in the landing page, displaying alanding keyboard in the landing page, where the landing keyboardincludes at least one key position, and each key position corresponds toa different operation; and determining a selected key position based onthe eye movement information and/or the EEG information, and executingan operation corresponding to the key position.

In a possible implementation, the method further includes: in a casewhere the eye movement information and/or the EEG information indicatespaying attention to a preset area in the search engine result page,displaying at least one search engine result keyboard, where each searchengine result keyboard includes at least one key position, and each keyposition corresponds to a different operation; and determining aselected key position based on the eye movement information and/or theEEG information, and executing an operation corresponding to the keyposition.

According to an aspect of the present disclosure, an method for websearch, applied to a server, includes: receiving a steady-state visualevoked potential sent by a user terminal, where the steady-state visualevoked potential is generated when a user gazes at keys on a querykeyboard of the user terminal; based on the steady-state visual evokedpotential, determining a character string inputted by the user and alanding page corresponding to the character string; and sending thelanding page to the user interface, such that the user terminal displaysthe landing page.

In a possible implementation, the method further includes: receiving EEGinformation sent by the user terminal, where the EEG information isgenerated when the user gazes at the landing page of the user terminal;based on the EEG information, detecting the user's feedback informationin response to the landing page, where the feedback information includesemotion information determined based on the EEG information; determininga search engine result page based on the feedback information, where thesearch engine result page includes at least two search resultscorresponding to the character string; and sending the search engineresult page to the user terminal, such that the user terminal displaysthe search engine result page.

In a possible implementation, the based on the steady-state visualevoked potential, determining a character string inputted by the userand a landing page corresponding to the character string, includes:based on the steady-state visual evoked potential, determining thecharacter string inputted by the user and at least one query termcorresponding to the character string; sending to the user terminal thecharacter string and the at least one query term corresponding to thecharacter string, such that the user terminal displays the characterstring and the at least one query term in the query suggestion module ofthe visual spelling page; receiving a search context sent by the userterminal, where the search context includes user state information; anddetermining the landing page based on the at least one query term andthe search context.

In a possible implementation, the determining a search engine resultpage based on the feedback information includes: determining adifference between a subject of each search result and a subject of eachof the landing page; and in a case where the feedback information isdissatisfaction, ranking the search results with a larger differencehigher than the search results with a smaller difference, or in a casewhere the feedback information is satisfaction, ranking the searchresults with a larger difference lower than the search results with asmaller difference.

In a possible implementation, the method further includes: receiving EEGinformation sent by the user terminal, where the EEG information isgenerated when the user views the search engine result page displayed bythe user terminal; detecting the user's preference information aboutsearch results in the search engine result page in real time based onthe acquired EEG information; re-ranking the search results in thesearch engine result page in real time based on the preferenceinformation; and sending the re-ranked search engine result page to theuser terminal, such that the user terminal displays the re-ranked searchengine result page in real time.

In a possible implementation, the based on the steady-state visualevoked potential, determining a character string inputted by the userand a query term corresponding to the character string includes:determining the character string inputted by the user based on thesteady-state visual evoked potential; and determining at least one queryterm corresponding to the character string by means of a candidate wordgeneration algorithm with massive information on the Internet.

In a possible implementation, the method further includes: receiving eyemovement information sent by the user terminal, and the based on thesteady-state visual evoked potential, determining a character stringinputted by the user, includes: determining the character stringinputted by the user based on the steady-state visual evoked potentialand the eye movement information.

In a possible implementation, the detecting the user's feedbackinformation in response to the landing page based on the EEG informationincludes: inputting the EEG information to a satisfaction predictingmodel to determine a degree of user satisfaction; determining acorresponding relationship between the degree of user satisfaction andeach text content in the landing page based on the eye movementinformation and/or the EEG information; and determining the feedbackinformation according to the degree of user satisfaction correspondingto each text content in the landing page.

According to an aspect of the present disclosure, an apparatus for websearch, applied to a user terminal, includes: a first displaying moduleconfigured to display a visual spelling page, where the visual spellingpage includes a query keyboard; a first acquiring module configured toacquire a steady-state visual evoked potential, where the steady-statevisual evoked potential is generated when a user gazes at keys on thequery keyboard; a first sending module configured to send thesteady-state visual evoked potential to a server, such that the server,based on the steady-state visual evoked potential, determines acharacter string inputted by the user, and a landing page correspondingto the character string; and a second displaying module configured todisplay the landing page in response to receiving the landing page sentby the server.

In a possible implementation, the apparatus further includes: a secondacquiring module configured to, after the landing page is displayed,acquire EEG information, where the EEG information is generated when theuser gazes at the landing page; a second sending module configured tosend the EEG information to the server, such that the server detects theuser's feedback information in response to the landing page based on theEEG information, and determines a search engine result page according tothe feedback information, where the feedback information includesemotion information determined based on the EEG information, and thesearch engine result page includes at least two search resultscorresponding to the character string; and a third displaying moduleconfigured to display the search engine result page in response toreceiving the search engine result page sent by the server.

In a possible implementation, the visual spelling page also includes aquery suggestion module, and the first sending module is configured to:send the steady-state visual evoked potential to the server, such thatthe server, based on the steady-state visual evoked potential,determines the character string inputted by the user and at least onequery term corresponding to the character string; in response toreceiving the character string and the at least one query term sent bythe server, display the character string and the at least one query termthrough the query suggestion module; acquire a search context in the EEGinformation, where the search context includes user state information;and send the search context to the server, such that the serverdetermines the landing page based on the at least one query term and thesearch context.

In a possible implementation, the apparatus is further configured to:acquire eye movement information, and the first sending module isconfigured to: send the steady-state visual evoked potential and the eyemovement information to the server, such that the server determines thecharacter string inputted by the user based on the steady-state visualevoked potential and the eye movement information.

In a possible implementation, the apparatus is further configured to: ina case where the eye movement information and/or the EEG informationindicates paying attention to a preset area in the landing page, displaya landing keyboard in the landing page, where the landing keyboardincludes at least one key position, and each key position corresponds toa different operation; and determine a selected key position based onthe eye movement information and/or the EEG information, and execute anoperation corresponding to the key position.

In a possible implementation, the apparatus is further configured to: ina case where the eye movement information and/or the EEG informationindicates paying attention to a preset area in the search engine resultpage, display at least one search engine result keyboard, where eachsearch engine result keyboard includes at least one key position, andeach key position corresponds to a different operation; and determine aselected key position according to the eye movement information and/orthe EEG information, and execute an operation corresponding to the keyposition.

According to an aspect of the present disclosure, an apparatus for websearch, applied to a server, includes: a first receiving moduleconfigured to receive a steady-state visual evoked potential sent by auser terminal, where the steady-state visual evoked potential isgenerated when a user gazes at keys on a query keyboard of the userterminal; a first determining module configured to, based on thesteady-state visual evoked potential, determine a character stringinputted by the user and a landing page corresponding to the characterstring; and a third sending module configured to send the landing pageto the user terminal, such that the user terminal displays the landingpage.

In a possible implementation, the apparatus further includes: a secondreceiving module configured to receive EEG information sent by the userterminal, where the EEG information is generated when the user gazes atthe landing page of the user terminal; a detecting module configured todetect the user's feedback information in response to the landing pagebased on the EEG information, where the feedback information includesemotion information determined based on the EEG information; a seconddetermining module configured to determine a search engine result pagebased on the feedback information, where the search engine result pageincludes at least two search results corresponding to the characterstring; and a fourth sending module configured to send the search engineresult page to the user terminal, such that the user terminal displaysthe search engine result page.

In a possible implementation, the first determining module is configuredto: based on the steady-state visual evoked potential, determine thecharacter string inputted by the user and at least one query termcorresponding to the character string; send the character string and theat least one query term corresponding to the character string to theuser terminal, such that the user terminal displays the character stringand the at least one query term in a query suggestion module of a visualspelling page; receive a search context sent by the user terminal, wherethe search context includes user state information; and determine thelanding page based on the at least one query term and the searchcontext. The landing page may be determined based on the search contextreceived from the user terminal (e.g., GPS position information of theuser), along with the search context derived from the EEG information bythe server (e.g., the mood of the user).

In a possible implementation, the second determining module isconfigured to: determine a difference between a subject of each searchresult and a subject of each of the landing page; in a case where thefeedback information is dissatisfaction, rank the search results with alarger difference higher than the search result with a smallerdifference, or in a case where the feedback information is satisfaction,rank the search results with a larger difference lower than the searchresult with a smaller difference.

In a possible implementation, the apparatus is further configured to:receive EEG information sent by the user terminal, where the EEGinformation is generated when the user views the search engine resultpage displayed by the user terminal; detect the user's preferenceinformation about the search results in the search engine result page inreal time based on the acquired EEG information; re-rank the searchresults in the search engine result page in real time based on thepreference information; and send the re-ranked search engine result pageto the user terminal, such that the user terminal displays the re-rankedsearch engine result page in real time.

In a possible implementation, the based on the steady-state visualevoked potential, determining a character string inputted by the userand the at least one query term corresponding to the character stringincludes: determining the character string inputted by the user based onthe steady-state visual evoked potential; and determining at least onequery term corresponding to the character string by means of a candidateword generation algorithm with massive information on the Internet.

In a possible implementation, the apparatus is further configured to:receive eye movement information sent by the user terminal, and thefirst determining module is configured to: determine the characterstring inputted by the user based on the steady-state visual evokedpotential and the eye movement information.

In a possible implementation, the detecting module is configured to:input the EEG information to a satisfaction predicting model todetermine a degree of user satisfaction; determine a correspondingrelationship between the degree of user satisfaction and each textcontent in the landing page based on the eye movement information and/orthe EEG information; and determine feedback information according to thedegree of user satisfaction corresponding to each text content in thelanding page.

According to an aspect of the present disclosure, an electronic deviceincludes a processor and a memory configured to storeprocessor-executable instructions, where the processor is configured tocall the instructions stored in the memory to execute the above method.

According to an aspect of the present disclosure, there is provided acomputer-readable storage medium having computer program instructionsstored thereon, where the computer program instructions, when executedby a processor, implement the above method.

In embodiments of the present disclosure, web search and browsing arepossible without hand-based operations. In addition, in the related art,the search engine result page containing a plurality of search resultsis directly displayed after the query term is inputted, and the userneeds to browse the search engine result page repeatedly, select andclick each search result that may meet the information needs, to get thepage meeting the information needs. In contrast, according to theembodiments of the present disclosure, the landing page that is mostlikely to meet the user's information needs is directly displayed afterthe character string is inputted, which helps to meet the user'sinformation needs more efficiently and effectively by using as few pagesas possible.

Furthermore, it helps to detect the user's feedback in response to thesearch in real time when displaying the search engine result pagesubsequently, so as to adjust the search results dynamically, therebyreducing complexity in user interactive operations during web search,and improving the search experience.

It should be understood that the above general descriptions and thefollowing detailed descriptions are only exemplary and illustrative, anddo not limit the present disclosure. Other features and aspects of thepresent disclosure may become apparent from the following detaileddescriptions of exemplary embodiments with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described here that are incorporated herein and constitutea part thereof are intended to illustrate embodiments in conformity withthe present disclosure and explain the technical solutions of thepresent disclosure together with the specification.

FIG. 1 illustrates an architecture schematic diagram of a web searchmethod according to an embodiment of the present disclosure.

FIG. 2 illustrates a flow chart of the web search method according to anembodiment of the present disclosure.

FIG. 3 illustrates a schematic diagram of a query keyboard according toan embodiment of the present disclosure.

FIG. 4 illustrates a schematic diagram of a landing page according to anembodiment of the present disclosure.

FIG. 5 illustrates a schematic diagram of a search engine result pageaccording to an embodiment of the present disclosure.

FIG. 6 illustrates a flow chart of another web search method accordingto an embodiment of the present disclosure.

FIG. 7 illustrates a schematic diagram of a web search method accordingto the related art.

FIG. 8 illustrates a schematic diagram of a web search method accordingto an embodiment of the present disclosure.

FIG. 9 illustrates a block diagram of a web search apparatus accordingto an embodiment of the present disclosure.

FIG. 10 illustrates a block diagram of another web search apparatusaccording to an embodiment of the present disclosure.

FIG. 11 illustrates a block diagram of an electronic device 800according to an embodiment of the present disclosure.

FIG. 12 illustrates a block diagram of an electronic device 1900according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Various exemplary embodiments, features and aspects of the presentdisclosure are described in detail below with reference to theaccompanying drawings. Same reference numerals in the drawings refer toelements with same or similar functions. Although various aspects of theembodiments are illustrated in the drawings, the drawings areunnecessary to draw to scale unless otherwise specified.

As used herein, the term “exemplary” is intended to be “used as anexample and an embodiment or illustrative”. Any embodiment describedherein as “exemplary” should not be construed as being superior orbetter than other embodiments.

As used herein, the term “and/or” is only an association relationshipdescribing the associated objects, indicating three relationships. Forexample, “A and/or B” may include three situations: A exists alone; bothA and B exist; and B exists alone. Furthermore, as used herein, the term“at least one of” means any one of a plurality of or any combinations ofat least two of a plurality of, for example, “including at least one ofA, B and C” may represent including any one or more elements selectedfrom a set consisting of A, B and C.

Furthermore, for better describing the present disclosure, numerousspecific details are illustrated in the following detailed description.Those skilled in the art should understand that the present disclosuremay be implemented without certain specific details. In some examples,methods, means, elements and circuits that are well known to thoseskilled in the art are not described in detail in order to highlight thesubject matter of the present disclosure.

FIG. 1 illustrates a schematic diagram of a web search method accordingto an embodiment of the present disclosure. As shown in FIG. 1 , asearch system in an embodiment of the present disclosure may include auser interaction module with a user terminal as the execution body, anda data processing module with a server as the execution body. The userterminal may include a user interface.

The user interaction module running on the user interface may beconfigured to display a controlled key position and a search engineresult, and send the collected information inputted by the user to theserver. Corresponding functional pages may include a visual spellingpage P1, a landing page P2, and a search engine result page P3. The dataprocessing module running on the server may receive and parse theinformation inputted by the user sent by the user interface in realtime, obtain user instructions or user feedback, and communicate withthe user interaction module running on the user interface.

From the perspective of interaction between the user and the searchsystem (including the user interaction module and the data processingmodule), the web search method shown in FIG. 1 may include threeinteractive behaviors: constructing a search query, examining a landingpage, and examining re-ranked search engine results.

In an interaction process of constructing a search query, examining alanding page, and examining re-ranked search engine results, the user'sinput information is information inputted without hand-based operations,which may include electroencephalogram (EEG) information and/or eyemovement information. The EEG information, including brain electricactivity or wave, may be collected through a Brain Computer Interface(BCI) of the user interface, and the eye movement information may becollected by an eye movement meter of the user interface. Theembodiments of the present disclosure may implement the interactionbetween the user and the search system based on the EEG information orthe eye movement information, and may also implement the interactionbetween the user and the search system by combining the EEG informationand the eye movement information, which is not limited herein.

For convenience of explanation, as an example, FIG. 1 briefly introducesthe three interactions: constructing a query, checking a landing page,and checking re-ranked search engine results, where the informationinputted by the user that is collected by the search system is the EEGinformation.

As shown in FIG. 1 , when constructing a query, the user may summarizeinformation needs into a character string and input the characterstring. For example, when the user wants to search for feline cheetah(“liebao” in phonetic Chinese), the user may summarize this informationneeds into a character string “liebao” or “lb”, and the like. Based on aquery inputting keyboard of the visual spelling page P1, the user mayinput the character string to the search system. When identifying theinput, the search system may determine the character string inputted bythe user without hand-based operations based on a steady-state visualevoked potential in the EEG information. The search system may directlysearch the character string inputted by the user. Alternatively, afterthe character string is obtained, a query suggestion module of thesearch system may provide search recommendation according to thecharacter string inputted by the user in combination with massive dataof the Internet, to help the user to perform query input quickly.

The EEG information generated may contain information needs and a searchcontext (such as user state, knowledge level, time, and position), whichis a user-side feature that cannot be captured by a search engine basedon the hand-based operation in the related art, and may be used todetermine the landing page P2 in a subsequent process of examining thelanding page.

As shown in FIG. 1 , when examining the landing page, a selection moduleof the server of the search system may retrieve on the Internetaccording to the EEG information obtained in the process of constructinga search query, and then select an optimal page (or lucky page) frompages related to the query as the landing page P2. For example, giventhat the character string inputted in the process of constructing asearch is “liebao”, the web pages related to the search may includepages introducing feline cheetah (“liebao” in phonetic Chinese), pagesof Cheetah (corresponding to “liebao” in phonetic Chinese) Browser, andpages related to Cheetah (corresponding to “liebao” in phonetic Chinese)Motors. In this case, assuming that it is analyzed from the searchcontext included in the EEG information that the user is in a workingstate, the pages of Cheetah Browser may be determined as the landingpage P2 from the pages related to the query. The selecting module mayuse a trained selecting model to select the pages related to the query,or may use a non-training method to perform the selection. A specificalgorithm for the selecting module is not limited herein.

After the search system determines the landing page P2, the landing pageP2 will be displayed in the user interface. When browsing the landingpage P2, the user may be satisfied and feel happy when browsing thehelpful information; and the user may be dissatisfied and feel annoyedwhen browsing the useless information. When the user browses the landingpage, the search system may collect the EEG information of the user inreal time and decode the EEG information so as to acquire thesatisfaction of the user and the emotion of the user in real time.

As shown in FIG. 1 , when checking re-ranked search engine results, are-ranking module of the search system may adjust a sequence ofrespective search results in the original search engine resultsaccording to the user satisfaction and user situations obtained whenchecking the landing page, and display a search engine result page P3.The re-ranking module may use the trained selecting model to re-rank thesearch results, and may also use the non-training method to perform there-ranking. A specific algorithm of the re-ranking module is not limitedherein.

It should be understood that although FIG. 1 does not show an eyemovement information, in order to improve the interaction efficiencybetween the user and the search system to improve the user experience,preferably, both the EEG information and the eye movement informationmay be used, where the EEG information is used mainly, supplemented bythe eye movement information, to help the user to interact with thesearch system efficiently.

For example, attention of the user may be tracked in real time based onthe eye movement information and EEG information. The attention of theuser may correspond to content of a display interface of the userinterface of the search system, such that the input speed and accuracyof characters may be improved at the visual spelling page P1 and theaction intention of the user may be identified based on the EEGinformation aided by the correspondence at the landing page P2 and thesearch engine result page P3. With the correspondence of finer and moregranular contents to the EEG information of the user, personalizedmodeling may be performed more accurately for the user to meet theinformation needs of the user, and improve the search experience.

FIG. 1 introduces architecture of a web search method in an embodimentof the present disclosure from the perspective of interaction betweenthe user and the search system. The web search method in an embodimentof the present disclosure is described below from the perspective of theuser interface and the server, respectively.

FIG. 2 illustrates a flow chart of a web search method according to anembodiment of the present disclosure. The web search method may beapplied to a user interface. The user interface may be User Equipment(UE), a mobile device, a user interface, a terminal, a cellular phone, acordless phone, a Personal Digital Assistant (PDA), a hand-held device,a computing device, a vehicle-mounted device, a wearable device, and thelike. The method may be implemented by invoking computer-readableinstructions ranked in a memory through a processor. As an example, theweb search method in the embodiment of the present disclosure isdescribed below by taking the user interface serving as an executionbody.

As shown in FIG. 2 , the web search method includes following steps S11to S14.

In step S11, a visual spelling page is displayed, where the visualspelling page includes a query inputting keyboard.

In step S12, a steady-state visual evoked potential in the EEGinformation is acquired, where the steady-state visual evoked potentialis generated when a user gazes at keys on the query inputting keyboard.

In step S13, the steady-state visual evoked potential is sent to theserver, such that the server, based on the steady-state visual evokedpotential, determines a character string inputted by the user, and alanding page corresponding to the character string.

In step S14, in response to receiving the landing page sent by theserver, the landing page is displayed.

For example, in the step S11, the visual spelling page may be displayedon a display of the user interface, and the visual spelling page mayinclude a virtual query inputting keyboard. The query inputting keyboardmay be a keyboard based on the Steady-State Visual Evoked Potential(SSVEP), and may establish a connection with the user's brain through aBrain Computer Interface (BCI) of the user interface, such that thequery inputting keyboard may be operated directly through the thoughtin/from the user's brain without hand-based operations.

The query inputting keyboard may include a plurality of key positions.Each key position has flicker with a different frequency, which maycorrespond to a different key position function. As the retina isstimulated with an intensity of flash or pattern in a visual field ofhuman, potential changes may be recorded in the visual cortex.Therefore, when the user pays attention to a key position flickering ata certain frequency, a visual area in the brain may be induced togenerate an SSVEP EEG signal with an equal frequency (or a multiple ofthe frequency), i.e., SSVEP harmonic waves.

In a scenario where the visual spelling page in the step S11 may displaythe query inputting keyboard, in order to acquire a key that is expectedto be clicked by the user in a case where the user cannot perform thehand-based operations (e.g., VR applications, disabled service), theSSVEP EEG signal generated when the user gazes at the key on the queryinputting keyboard may be acquired in the step S12. The SSVEP EEG signalis a response of human to a visual stimulation at a certain frequency.When the retina is subjected to a visual stimulation of 3.5 Hz to 75 Hz,the visual cortex of the brain may generate EEG signals with the samefrequency as the visual stimulation (or a multiple of the frequency).

As an example, the query inputting keyboard contains 33 stimulationfrequencies, and FIG. 3 illustrates a schematic diagram of the queryinputting keyboard according to an embodiment of the present disclosure.As shown in FIG. 3 , the frequency range of a flicker block (i.e., thekey position) of the query inputting keyboard may be 8-15.68 Hz. One keyposition may be arranged every 0.24 Hz, and a phrase difference betweenevery two adjacent key positions is 0.5π. The QWERT keyboard layout,i.e., a full keyboard layout with first six letters Q, W, E, R, T, Y inthe first row, may be used. The search keyboard includes 5 numeric keys(key positions from “1” to “5”), 26 letter keys (key positions from “a”to “z”) and 2 functional keys (a delete key “Del” and a search key“Search”).

When the visual spelling page displays the query inputting keyboardshown in FIG. 3 , the user may gaze at the flicker key position “a” inthe query inputting keyboard to input the character string “a”, and theuser interface may acquire the SSVEP EEG signal corresponding to the keyposition “a”. Similarly, SSVEP EEG signals corresponding to differentkey positions may be acquired by gazing at different key positions onthe query inputting keyboard.

The user interface may send the SSVEP EEG information acquired in realtime in the step 12 to the server in real time in the step 13. Theserver may invoke an identifying method stored therein to analyze thereceived SSVEP EEG signal in real time, to determine the user'sintention on the key position to be inputted, thereby determining thecharacter string inputted by the user.

The SSVEP EEG identifying algorithm stored by the server may include anon-training method and a supervised training method.

The non-training method may include Minimum Energy Combination (MEC),Canonical Correlation Analysis (CCA), Multivariate Synchronization Index(MSI), Filter Bank Canonical Correlation Analysis (FBCCA), CanonicalVariates with Auto Regressive Spectral Analysis (CVARS), and the like.

The supervised training method may include Task-Related ComponentAnalysis (TRCA), Multi-Stimulus Task-Related Component Analysis(MSTRCA), Extended CCA, modified Extended CCA (m-Extended CCA),L1-regularized Multiway CCA (L1MCCA) and Individual Template-Based CCA(ITCCA), and the like.

It should be understood that the present disclosure does not limit aspecific method for identifying the character string inputted by thequery inputting keyboard according to the SSVEP EEG information.

The method for identifying the character string inputted by the queryinputting keyboard according to the SSVEP EEG information is describedbelow by taking the Canonical Correlation Analysis (CCA) as an example.

The CCA method is a non-training method for calculating a typicalcorrelation coefficient between the SSVEP EEG signal collected in realtime and a reference signal (a theoretical SSVEP EEG signal induced byflickers at different frequencies). The reference signal may include aconstructed sine and cosine signal, and a flicker block (key position)corresponding to a maximal value of this correlation coefficient is theinput intention of the user.

The SSVEP signal may be expressed by a matrix S:

S=(x ₁ ,x ₂ ,x ₃ , . . . x ₉)^(T)  (1)

In the formula (1), the matrix S is a matrix of 9*Ns, where Ns is thenumber of sampling points, and each row of the matrix represents asignal channel for EEG collection.

The reference signal R_(f) may be expressed by a matrix of Ns*10, whereNs is the number of sampling points in the SSVEP signal, and thereference signal R_(f) may be expressed by the following formula:

$\begin{matrix}{{R_{f} = \begin{bmatrix}{\sin\left( {2\pi{ft}} \right)} \\{\cos\left( {2\pi{ft}} \right)} \\ \vdots \\{\sin\left( {2\pi{Nft}} \right)} \\{\cos\left( {2\pi{Nft}} \right)}\end{bmatrix}},{t = \frac{1}{F_{s}}},{\frac{2}{F_{s}}\ldots\frac{N_{s}}{F_{s}}}} & (2)\end{matrix}$

In the formula (2), N is the number of harmonic waves. For example, Nmay be set to 10. f is a frequency of the reference signal. Fs is asampling rate, and Ns is the number of sampling points.

For each flicker frequency from 8 Hz to 15.68 Hz (such as f=8.00, 8.24,8.48 . . . 15.68), the reference signal R_(f) may be generated by usingthe above formula. The correlation between each reference signal R_(f)and the SSVEP EEG signal S is then calculated. After calculation, thereference signal R_(f) with the highest correlation may be selected asan identification result, that is, a target key position that the userintends to input may be obtained, and then the character string inputtedby the user is obtained.

After the server determines the character string inputted by the user,the server may directly search the character string inputted by the useron the Internet, and determine a page with the highest correlation tothe character string inputted by the user as the landing page. Forexample, if the character string inputted by the user is “cctv”, theserver may directly search “cctv” to obtain the landing pagecorresponding to “cctv”, i.e., the official website page of ChinaCentral Television (CCTV).

Alternatively, the server may determine at least one query termcorresponding to the character string based on the character string. Theserver may then search on the Internet based on the at least one query(at least one term) corresponding to the character string to obtain thelanding page corresponding to the character string.

In a possible implementation, the visual spelling page also includes aquery suggestion module, and the step S13 may include:

a step S131: sending the steady-state visual evoked potential to theserver, such that the server, according to the steady-state visualevoked potential, determines the character string inputted by the userand at least one query term corresponding to the character string;

a step S132: in response to receiving the character string and the atleast one query term sent by the server, displaying the character stringand the at least one query term by the query suggestion module;

a step S133: acquiring a search context in the EEG information, wherethe search context includes user state information, knowledge level,position, time, etc.; and

a step S134: sending the search context to the server, such that theserver determines the landing page based on the at least one query termand the search context.

For example, the visual spelling page of the user interface may displaythe query inputting keyboard for inputting the character string withouthand-based operations, and may also include the query suggestion moduleconfigured to perform query recommendation according to the characterstring inputted by the user in combination with massive data of theInternet.

In the step S131, the user interface may send the steady-state visualevoked potential to the server, and the server determines the characterstring inputted by the user based on the steady-state visual evokedpotential. The specific process may refer to the above SSVEP EEGinformation classification or identification algorithm, which is notrepeated herein.

The server receives the character string, and may determine at least onequery term corresponding to the character string according to thecharacter string. In a general search scenario, a user may have acomplicated search intention, so the character string may hardly matchthe information needs of the user. Therefore, the query recommendationtechnology stored by the server may be used to provide candidatequeries, to help to find the desirable query term of the user with lesseffort. The search recommendation technology may use a trainedrecommendation model to acquire at least one query term. Massive networkdata and large-scale heterogeneous data on the Internet, combined withuser behavior analysis such as user clicks, query formulation, hot newstracking and so on, are used in the process of training therecommendation model, which helps to reduce the difference between theuser intention and the candidate queries effectively.

For example, assuming that the character string inputted by the user is“liebao”, the “liebao” may be inputted to the recommendation modelstored in the server, and at least one query term corresponding to thecharacter string may be obtained, such as cheetah, Cheetah Browser,Cheetah Motors, and the like.

In the step S132, after analyzing the character string and the at leastone query term corresponding to the character string, the server maysend the character string and the at least one query term correspondingto the character string to the user interface. In response to receivingthe character string and the at least one query term sent by the server,the user may select and display the character string and the at leastone query term in the query suggestion module.

For example, the server may also send the query terms such as cheetah,Cheetah Browser, Cheetah Motors and the like to the user interface, andthe query suggestion module of the visual spelling page in the userinterface may display the query terms such as cheetah, Cheetah Browser,Cheetah Motors and the like.

In the step S133, the user terminal may acquire a search context in theEEG information, and the search context may be a search contextcollected when the user gazes at the query keyboard, or a search contextcollected when the user gazes at the query suggestion module. The searchcontext may be used to determine state information (such as a reststate, an entertainment state, a sports state, or a working state) ofthe user. The search context in the EEG information may be extracted bya pre-trained machine learning model or relevant algorithms, which isnot limited herein.

In the step S134, the search context is sent to the server, and theserver determines the landing page according to the at least one queryterm and the search contexts.

For example, the user interface may collect the EEG information when theuser gazes at the query suggestion module and send it to the server inreal time. The server may analyze the received EEG information generatedwhen the user gazes at the query suggestion module. Assuming that theserver analyzes that the user is currently in a working state. Among thequery terms such as cheetah, Cheetah Browser, Cheetah Motors, and thelike, the user's search intent may be related to Cheetah Browser. Assuch, search may be performed on “Cheetah Browser”, and an officialwebsite of the Cheetah Browser may be selected as the landing page.

For another example, it is possible to analyze the user's attention tocheetah, Cheetah Browser and Cheetah Motors through eye movement signalsof the user, and search for the query term with the highest attention todetermine the landing page.

Therefore, in comparison to the related art where it is necessary to usean input method (Pinyin input method or Wubi input method) to convertthe character string into a key word, and then provide queryrecommendations for the key word, in an embodiment of the presentdisclosure, by inputting the character string, at least one query termmay be directly displayed on the user interface to directly providequery recommendations, which helps to improve the search efficiency andthe user experience.

In the step S13, the server determines the landing page. In the stepS14, the user interface may display the landing page on the display ofthe user interface in response to receiving the landing page sent by theserver. For example, given that the landing page is the official websiteof the Cheetah Browser, the official website page of the Cheetah Browsermay be displayed.

Through the steps S11 to S14, web pages may be retrieved and browsedwithout hand-based operations. Moreover, in comparison to the relatedart where a Search Engine Result Page (SERP) containing a plurality ofsearch results is directly displayed after a query term is inputted, andit is necessary to browse the search engine result page repeatedly toselect and click each search result that may meet the information needsof the user, to get the page that meet the information needs of theuser; according to the embodiments of the present disclosure, after thecharacter string is inputted, a landing page that is most likely todirectly meet the information needs of the user may be displayed, whichhelps to meet the information needs of the user with minimal pages,making the search process efficient and rapid.

Furthermore, it is conducive to detecting search feedback in real timewhen the search engine result page is displayed subsequently, to adjustthe search results dynamically, reducing complexity in an interactiveoperation of the user during network search, and improving the searchexperience of the user.

It should be understood that in the steps S11 to S14, without hand-basedoperations, web page search may be performed based on the EEGinformation, and may also be performed using both the EEG informationand the eye movement information, thereby improving the searchexperience.

In a possible implementation, the acquiring the eye movementinformation, and sending the steady-state visual evoked potential to theserver, such that the server, based on the steady-state visual evokedpotential, determines the character string inputted by the user,includes: sending the steady-state visual evoked potential and the eyemovement information to the server, such that the server determines thecharacter string inputted by the user based on the steady-state visualevoked potential and the eye movement information.

The eye movement information may include an eyeball position and aneyeball movement feature tracked and measured by an eye movement meter.The eye movement information may be used to track an attention of theuser in real time, to correspond the attention of the user to thecontent on a display interface.

For example, assuming that the user wants to input the character “a”,eyes of the user may be focused on a key position “a” (such as a firstkey position of a third row in FIG. 3 ) of the query inputting keyboardon the display, and the user interface may acquire the eye movementinformation and the SSVEP EEG information of the user.

The user interface may send the eye movement information and the SSVEPEEG information to the server. After receiving these two kinds ofinformation, the server may analyze the eye movement information and theSSVEP EEG information, and may determine the character inputted by theuser as “a” in a case where the position indicated by the eye movementinformation corresponds to the position of the key position “a”, and thefrequency of the SSVEP EEG information is the same as (or a multiple of)the flicker frequency of the key position “a”, which helps to improvethe character input accuracy. The user may also determine the characterinputted by the user as “a” when the position indicated by the eyemovement information corresponds to the position of the key position“a”, or the frequency of the SSVEP EEG information is the same as (or amultiple of) the flicker frequency of the key position a, which helps toimprove the character input efficiency. It should be understood that thepresent disclosure does not limit a specific method for determining theinputted character based on the eye movement information or the EEGinformation.

In this way, the character input speed and accuracy may be improved.

After the landing page is displayed by the steps S11 to S14, if the useris dissatisfied with the displayed landing page, the search engineresult page may also be acquired through the steps S15 to S17 so as tomeet the information needs of the user.

In a possible implementation, the steps S15 to S17 include:

step S15: acquiring the EEG information, where the EEG information isgenerated when the user gazes at the landing page;

step S16: sending the EEG information to the server, such that theserver detects the user's feedback information in response to thelanding page based on the EEG information, and determines the searchengine result page based on the feedback information;

wherein the feedback information includes emotion information determinedbased on the EEG information, and the search engine result page includesat least two search results corresponding to the character string; and

step S17: in response to receiving the search engine result page sent bythe server, displaying the search engine result page.

For example, given that the character string inputted by the user is“liebao”, the landing page displayed by the user interface is a pageintroducing the cheetah (an animal belonging to the feline acinonyx). Inthe step S15, the user interface may collect the EEG informationgenerated when the user browses the page introducing the cheetah animal.

In the step S16, the user interface may send the collected EEGinformation, i.e., the EEG information generated when the user browsesthe page introducing the cheetah animal to the server. The server maydetect the user's feedback information in response to the pageintroducing the cheetah animal based on the received EEG information,and the feedback information may include emotion information of the userin response to the page introducing the cheetah animal. The emotioninformation may indicate the degree of user satisfaction in response tothe landing page, including satisfaction or dissatisfaction.

If the feedback information is dissatisfaction, after the user closesthe page, a search engine result page including at least two searchresults corresponding to “liebao” may be determined. For example, thesearch engine result page may include search results such as CheetahBrowser, Cheetah Motors, and the like. In the search engine result page,the search results with a larger difference from the landing page interms of a subject will be ranked in top positions, thus morediversified search results are ranked in the top positions to meet theinformation needs of the user effectively.

If the feedback information is satisfaction, after the user closes thepage, the search results related to this landing page will be ranked intop positions. For example, in the search engine result page, the searchresults related to the cheetah animal (for example, including regionaldistribution of the cheetah, classification of the cheetah and othersearch results) may be ranked in top positions. The system may detectthe feedback signal as “satisfaction” about the cheetah animal.

The emotion information in the EEG information may be extracted by apre-trained machine learning model or relevant algorithms, which is notlimited herein.

In the step S17, after determining the search engine result page, theserver may send the search engine result page to the user interface. Theuser interface may display the search engine result page in the display.For example, the user interface may display the search engine resultpage including the search results such as Cheetah Browser, CheetahMotors and the like in the display.

In this way, in a case where the landing page cannot meet theinformation needs of the user, more and richer search results may beprovided to meet the information needs of the user, thereby improvingthe user experience.

In the process of steps S15 to S17, the user may perform an interactiveoperation on the landing page and the search engine result page.

In a possible implementation, the method further includes: in a casewhere the eye movement information and/or the EEG information indicatespaying attention to a preset area in the landing page, displaying alanding keyboard in the landing page, where the landing keyboardincludes at least one key position, and each key position corresponds toa different operation; and based on the eye movement information and/orthe EEG information, determining a selected key position, and executingan operation corresponding to the key position.

For example, FIG. 4 illustrates a schematic diagram of the landing pageaccording to an embodiment of the present disclosure. As shown in FIG. 4, when the whole landing page is viewed completely or the current pagedisplayed on the screen is viewed completely, the user may move thesight to a blank area (a preset area) at the right side of the web page,the search system may display the landing keyboard automatically, andrespective key positions (which may be flicker blocks with differentflickering frequencies) displayed on the landing keyboard may be used toexecute operations on the landing page. For example, the landingkeyboard may include 3 key positions, i.e. flicker blocks correspondingto functions such as “close”, “slide up”, “slide down” and the likeshown at the right side of FIG. 4 .

The preset area may be a blank area on the right side, or an area with apreset icon. The preset area is not limited herein.

It should be understood that the flicker blocks of the landing keyboardmay have the same working principle as the key positions in the queryinputting keyboard in FIG. 3 , and may refer to the SSVEP EEGinformation recognition algorithm of the query inputting keyboard above.In addition, a dual-modality feature of the EEG information and the eyemovement information may also be used to determine the control logic,which is described above. The flicker blocks may be any frequency from3.5 Hz to 75 Hz, and the size, color and specific frequency of theflicker blocks are not limited herein.

Furthermore, the user interface may collect the EEG information of theuser in real time and send it to the server, and the server may detectthe degree of satisfaction, emotion and the like of the user in realtime when the user browses the page. If the server detects“dissatisfaction” as the user's feedback during browsing, the server mayskip to the search engine result page to display more diversified searchresults after the user executes a closing operation.

In this way, web pages may be browsed and interacted without hand-basedoperations, which is helpful for the physically challenged who cannotuse the keyboard or mouse and the healthy group in a special scenario(two hands are in a non-idle state) to normally browse web pages.

In a possible implementation, in a case where the eye movementinformation and/or the EEG information indicates paying attention to apreset area in the search engine result page, at least one search engineresult keyboard is displayed, where each search engine result keyboardincludes at least one key position, and each key position corresponds toa different operation. Based on the eye movement information and/or theEEG information, the selected key position is determined, and theoperation corresponding to the key position is executed.

For example, FIG. 5 illustrates a schematic diagram of the search engineresult page according to an embodiment of the present disclosure. Asshown in FIG. 5 , when the user browses the search engine result page,each key position (which may be a flicker block) in FIG. 5 may be usedto execute an operation on the search engine result page. When the sightof the user is moved to the blank area (the preset area) on the rightside, besides the flicker blocks of the three functional keys of“close”, “slide-up”, and “slide-down”, the display interface may alsodisplay the flicker block with a fixed frequency, to bind a clickbehavior of the user on the result. The flicker blocks including threefunctional keys of close, slide-up, and slide-down may be regarded asone keyboard, and a plurality of flicker blocks that are bonded with thecurrent search result may be regarded as one keyboard. The presentdisclosure does not limit the number of the search engine resultkeyboards, and the number of key positions included in the search engineresult keyboard.

The preset area may be a blank area at the right side, or an area with apreset icon. The preset area is not limited herein.

It should be understood that the flicker blocks of the search engineresult keyboard may have the same working principle as the key positionsin the query inputting keyboard in FIG. 3 and may refer to the SSVEP EEGinformation recognition algorithm of the query inputting keyboard above.In addition, the dual-modality feature of the EEG information and theeye movement information may also be used to determine the controllogic, which is not repeated herein. The flicker block may be anyfrequency from 3.5 Hz to 75 Hz, and the size, color, and specificfrequency of the flicker block are not limited herein.

Furthermore, when the user slides down the search engine result page,the EEG information in response to the landing page and the displayedsearch result may be considered comprehensively in the search results,to adjust the ranking strategy of the newly-loaded content dynamically.

In this way, interactive operations of web page search and browsing maybe performed without hand-based operations, which is helpful for thedisabled people who cannot use the keyboard or mouse, and the healthypeople in a special scenario (two hands are in a non-idle state) tonormally browse the web pages.

Through the steps S11 to S17, the web search method in an embodiment ofthe present disclosure is described by taking the user interface as anexecution body. The web search method in an embodiment of the presentdisclosure is described below by taking the server as an execution body.

FIG. 6 illustrates a flow chart of the web search method according to anembodiment of the present disclosure. The method is applied to theserver. As shown in FIG. 6 , the method includes following steps.

In step S21, a steady-state visual evoked potential sent by a userinterface is received, where the steady-state visual evoked potential isgenerated when the user gazes at keys on a query inputting keyboard ofthe user interface.

In step S22, based on the steady-state visual evoked potential, acharacter string inputted by the user and a landing page correspondingto the character string are determined.

In step S23, the landing page is sent to the user interface, such thatthe user interface displays the landing page.

For example, the steady-state visual evoked potential received by theserver is generated when the user gazes at a query inputting keyboardshown in FIG. 3 . The server may receive SSVEP EEG information sent bythe user interface in real time. For example, if the user gazes at a keyposition “1” on the query inputting keyboard first, the server receivesthe SSVEP EEG information corresponding to the key position 1 in realtime, and determines the character inputted by the user as “1” accordingto the SSVEP EEG information. If the user gazes at a key position “b” onthe query inputting keyboard, the server receives the SSVEP EEGinformation corresponding to the key position b in real time, anddetermines the character inputted by the user as “b” according to theSSVEP EEG information. It may be seen that the character string inputtedby the user may be determined as “lb” according to the SSVEP EEGinformation received in real time, and the character string “lb” may besearched for directly on the Internet to determine the landing page(such as a page introducing cheetah the animal) corresponding to “lb”.

After determining the landing page, the server may send the landing pageto the user interface, and the user interface displays the landing page.

Through the steps S21 to S23, the web pages may be retrieved and browsedwithout hand-based operations. In the related art, the search engineresult page containing a plurality of search results is directlydisplayed after the query term is inputted, and it is necessary tobrowse the search engine result page repeatedly, select and clickvarious search results that may meet the user's information needs, andobtain a page meeting the user's information needs. In contrast,according to the embodiments of the present disclosure, after thecharacter string is inputted by the user, the landing page most likelyto meet the user's information needs may be determined directly, whichhelps to meet the user's information needs with minimal pages, therebymeeting the user's information needs efficiently and rapidly, reducingcomplexity in the interactive operations during network search, andimproving the search experience.

It should be understood that in the above process, in the step S22,retrieval on the character string may be directly performed on theInternet to determine the character string. In order to improve theaccuracy of the retrieval, the query recommendations may also beperformed firstly based on the character string, and then the retrievalis performed on the Internet.

In a possible implementation, the step S22 may include: based on thesteady-state visual evoked potential, determining the character stringinputted by the user and at least one query term corresponding to thecharacter string; sending the character string and the at least onequery term corresponding to the character string to the user interface,so that the user interface displays the character string and the atleast one query term in a query suggestion module of a visual spellingpage; receiving a search context sent by the user interface, where thesearch context includes user state information; and determining thelanding page based on the at least one query term and the searchcontext.

For example, the based on the steady-state visual evoked potential,determining the character string inputted by the user and the at leastone query term corresponding to the character string, includes:determining the character string inputted by the user based on thesteady-state visual evoked potential; and determining at least one queryterm corresponding to the character string based on a candidate wordgeneration algorithm with massive information on the Internet.

The server may determine one character string according to the receivedSSVEP EEG information, and may determine at least one query termcorresponding to the character string.

In a general search scenario, a user generally has a complicated searchintention, and the character string may hardly match the informationneeds of the user accurately. Therefore, the search recommendationtechnology stored in the server may be used. For example, a candidateword generation algorithm with massive information on the Internet maybe used to provide candidate queries to help the user to find thedesirable query term with less effort.

The candidate word generation algorithm with massive information on theInternet may acquire at least one query term through a trainedrecommendation model. Massive network data and large-scale heterogeneousdata on the Internet, combined with user behavior analysis such asclicks, query formulation, hot news tracking and so on are used in theprocess of training the recommendation model, which can reduce thedifference between the user intention and the candidate querieseffectively, and obtain at least one query term meeting the userintention.

For example, if the character string inputted by the user is “liebao”,“liebao” may be inputted to the recommendation model stored in theserver, and the query term corresponding to the character string isobtained, including cheetah, Cheetah Browser, Cheetah Motors, and thelike.

After analyzing the character string and the at least one query termcorresponding to the character string, the server may send the characterstring and the at least one query term corresponding to the characterstring to the user interface. In response to receiving the characterstring and the at least one query term sent by the server, the userinterface may display the character string and the at least one queryterm in the query suggestion module.

For example, the server may also send the query terms including cheetah,Cheetah Browser, Cheetah Motors and the like to the user interface, andthe query terms including cheetah, Cheetah Browser, Cheetah Motors andthe like may be displayed in the query suggestion module of the visualspelling page of the user interface, and the user interface may acquirea search context in the EEG information, and the search context may be asearch context collected when the user gazes at the query inputtingkeyboard, or a search context collected when the user gazes at the querysuggestion module. The search context may be used to determine stateinformation (such as a rest state, an entertainment state, a sportsstate, or a working state) of the user.

The server may receive the search context sent by the user interface,and determine the landing page based on the at least one query term andthe search context. For example, the user interface may collect the EEGinformation when the user gazes at the query suggestion module and sendit to the server in real time. The server may analyze the received EEGinformation when the user gazes at the query suggestion module. If it isanalyzed by the server that the user is in a working state at presentand the user intends to use the Cheetah Browser to work among the queryterms such as cheetah, Cheetah Browser, Cheetah Motors, and the like,then the search may be performed on the topic of Cheetah Browser, and anofficial website of the Cheetah Browser is determined as the landingpage.

Therefore, in comparison to the related art where it is necessary to usean input method (Pinyin input method or Wubi input method) firstly toconvert the character string into a key word, and then provide queryrecommendations for the key word, query recommendations are realizeddirectly by inputting the character string in the embodiment of thepresent disclosure, since at least one query term may be displayed onthe user interface by inputting the character string, which helps toimprove the query efficiency and the user experience.

It should be understood that in the above process, the server maydetermine the character string inputted by the user based on thesteady-state visual evoked potential; the server may also receive theeye movement information sent by the user interface, and determine thecharacter string inputted by the user based on the steady-state visualevoked potential and the eye movement information. The method fordetermining the character string inputted by the user based on thesteady-state visual evoked potential and the eye movement informationmay refer to the relevant part of the user interface, which is notrepeated herein.

The character string is inputted by using both the EEG information andthe eye movement information, which may improve the efficiency andaccuracy of character string input.

In the steps S21 to S23, after the landing page is determined, the userinterface may display the landing page, and a degree of usersatisfaction in response to the landing page may be detected in realtime through the received EEG information. The search engine result pagemay also be determined through the steps S24 to S27, to meet theinformation needs of the user. The search result may be adjusteddynamically according to the user's feedback about the search that isdetected in real time.

In a possible implementation, the steps S24 to S27 include:

in the step S24, the EEG information sent by the user interface isreceived, where the EEG information is generated when the user gazes atthe landing page of the user interface;

in the step S25, the user's feedback information in response to thelanding page is detected based on the EEG information, where thefeedback information includes emotion information determined based onthe EEG information;

in the step S26, the search engine result page is determined based onthe feedback information, where the search engine result page includesat least two search results corresponding to the character string; and

in the step S27, the search engine result page is sent to the userinterface, such that the user interface displays the search engineresult page.

For example, assuming that the character string inputted by the user is“liebao”, the landing page displayed by the user interface is a pageintroducing the cheetah (an animal belonging to the feline acinonyx).

In the S24, the server may receive the EEG information sent by the userinterface in real time, and the EEG information received by the serveris the EEG information generated when the user browses the pageintroducing cheetah the animal.

In the step S25, the server may detect the user's feedback informationin response to the page introducing cheetah the animal based on thereceived EEG information, and the feedback information may include theuser's emotion information in response to the page introducing cheetahthe animal.

In a possible implementation, the step S25 includes: inputting the EEGinformation to a satisfaction predicting module to determine a degree ofuser satisfaction; based on the eye movement information and/or the EEGinformation, determining a corresponding relationship between the degreeof user satisfaction and each text content in the landing page; anddetermining the feedback information based on the degree of usersatisfaction corresponding to each text content in the landing page.

For example, the server may receive, in real time, the EEG informationgenerated when the user browses an optimal landing page, and may inputthe obtained EEG information to the satisfaction predicting modelobtained by supervised training, and the satisfaction predicting modelis used to analyze the EEG information of the user to estimate thedegree of user satisfaction in real time.

For example, given that the landing page is a page introducing cheetahthe animal, and the trained satisfaction predicting model is stored instorage of the server, the server may receive the EEG informationgenerated when the user browses the cheetah in real time, and input theEEG information acquired in real time to the satisfaction predictingmodel. As the page introducing cheetah the animal contains massiveinformation, when the user is satisfied with the browsed text content(phrases, sentences, paragraphs, and the like), the degree of usersatisfaction outputted by the satisfaction predicting model is higherthan a preset threshold. When the user is dissatisfied with the browsedtext content (phrases, sentences, paragraphs, and the like), the degreeof user satisfaction outputted by the satisfaction predicting model islower than the preset threshold.

The EEG information generated when a large number of users browse theweb pages and corresponding satisfaction tags and labels may be used asthe training data of the satisfaction predicting model, and the trainingdata may be used to train the satisfaction predicting model to obtainthe trained satisfaction predicting model.

It should be understood that the EEG information included in thetraining data may contain one or more features of time domain, frequencydomain and space domain, and the satisfaction predicting model may be aseries of classification or regression models such as a traditionalmodel, a neural network model and the like, and may be used to predictwhether the user is satisfied or a degree of user satisfaction. Thepresent disclosure does not limit the specific content of the trainingdata and the specific structure of the satisfaction predicting model.

The server may detect the degree of user satisfaction in real time byanalyzing the EEG information through the satisfaction predicting model,and the server may also acquire the text content of the user's attentionin real time based on the eye movement information. Therefore, thedegree of user satisfaction may correspond to more fine-grained textcontent (phrases, sentences, paragraphs, and the like) by combining eyemovement behavior contained in the user's eye movement information withthe EEG information. For example, if it is determined by the server thatthe user is interested in a first phrase in the N^(th) row of the pagethrough the eye movement behavior, and then a text content correspondingto the position of interest and a degree of user satisfaction inresponse to the text content are determined.

Based on the satisfaction corresponding to each text in the landingpage, the feedback information may be determined. The feedbackinformation may include the user's emotion information in response tothe page introducing the cheetah animal, and the emotion information isthe degree of user satisfaction in response to each text in the landingpage.

In this way, the degree of user satisfaction may correspond to the morefine-grained text content, which helps to subsequently acquire thesearch engine result page that may better meet the information needs ofthe user.

In the step S26, if the feedback information is dissatisfaction, afterthe user closes the page, a search engine result page including at leasttwo search results corresponding to “liebao” may be determined. Forexample, the search engine result page may include Cheetah Browser,Cheetah Motors and other search results.

In a possible implementation, the step S26 includes: determining adifference between a subject of each search result and a subject of eachof the landing page; in a case where the feedback information isdissatisfaction, ranking the search results with a larger differencehigher than the search results with a smaller difference, or in a casewhere the feedback information is satisfaction, ranking the searchresults with a larger difference lower than the search results with asmaller difference.

For example, assuming that the landing page is a page introducing thecheetah animal, if the user's feedback information in response to thelanding page is dissatisfaction, it means that the topic of the landingpage, that is, the cheetah animal, is not the information the userexpects to acquire. The search engine result page includes varioussearch results corresponding to the character string “liebao”. Forexample, the search engine result page may include related subjects suchas the cheetah animal, Cheetah Browser, Cheetah Motors and other searchresults.

The server may determine a difference between a subject of each searchresult in the search engine result page and a subject of each of thelanding page. For example, if the difference between the search resultsof the subjects such as Cheetah Browser and Cheetah Motors and thesubject of the landing page is high, ranking of the search results ofthe subjects such as Cheetah Browser and Cheetah Motors may be raised.If the difference between the search results of the subjects such as thecheetah animal (such as classification of the cheetah, color of thecheetah and other subjects) and the subject of the landing page is low,ranking of the search results of the subjects such as the cheetah animalmay be lowered.

If the user's feedback information in response to the landing page issatisfaction, the subject of the cheetah animal of the landing page isthe information the user expects to find. When the user wants to browsemore content after closing the landing page, the server may rank searchresults with a smaller difference in the search engine result page fromthe subject of the landing page in the top positions to meet theinformation needs of the user.

In this way, the ranking of the search results in the search engineresult page is adjusted according to the user's feedback information inresponse to the landing page, which helps to find the content to meetthe user's information needs as soon as possible.

In the step S27, after determining the search engine result page, theserver may send the search engine result page to the user interface, andthe user interface may display the search engine result page on thedisplay. For example, the search engine result page including the searchresults such as Cheetah Browser, Cheetah Motors and the like may bedisplayed on the user interface.

In this way, more and richer search results meeting the user'sinformation needs may be provided in a case where the landing pagecannot meet the user's information needs, thereby improving the userexperience.

In the search engine result page, if the search results in top positionscannot meet the information needs of the user sufficiently, in order tofurther improve the search experience, the EEG information of the usermay correspond to different search results to analyze the user'spersonalized preference for different search results, and re-rank eachsearch result included in the search engine result page in real time.

In a possible implementation, the EEG information sent by the userinterface is received in real time, where the EEG information isgenerated when the user browses the search engine result page displayedby the user interface; the user's preference information about thesearch result in the search engine result page is detected in real timebased on the acquired EEG information; re-ranking of individual searchresults in the search engine result page is performed in real time basedon the preference information; the re-ranked search engine result pageis sent to the user interface, and the user interface displays there-ranked search engine result page in real time.

For example, assuming that the search engine result page includesvarious search results corresponding to the character string “liebao”,the server may receive the EEG information sent by the user interface inreal time, and the EEG information is generated when the user browsesthe search engine result page displayed by the user interface.

The server may input the acquired EEG information to a trained networkmodel for detecting the user's preference information, to detect theuser's preference information about the search results in the searchengine result page in real time. For the training method of the networkmodel for detecting the user's preference information, reference is madeto the satisfaction predicting model stated above, which is not repeatedherein. The present disclosure does not limit the structure of thenetwork model for detecting the user's preference information.

Re-ranking of respective search results in the search engine result pagemay be performed in real time based on the obtained user's preferenceinformation about different search results. For example, assuming thatthe search engine result page includes search results related to thecheetah animal, Cheetah Browser and Cheetah Motors, the detectedpreference information is the search result related to the CheetahMotors, and when the user selects to slide down the page to load moresearch results, the loaded content may be dynamically adjusted, and thesearch results related to the Cheetah Motors may be displayed in toppositions.

The server may send the re-ranked search engine result page to the userinterface, and the user interface may display the re-ranked searchengine result page in real time.

In this way, the user's information needs are sufficiently satisfied.

In order to better understand the effect of the embodiment of thepresent disclosure, the network search method in the related art and thesearch method in the embodiment of the present disclosure are comparedand described below.

FIG. 7 illustrates a schematic diagram of the web search methodaccording to the related art. As shown in FIG. 7 , in the search systemof the related art, the inputted character string is converted into thequery term through an input method firstly, and the query term may beretrieved in the network. After the query term is inputted, the searchsystem may display a search engine result page including a plurality ofsearch results directly. Thus a user needs to browse and operate thesearch engine result page repeatedly to select a search result meetingthe information needs, and the user also needs to access the landingpage corresponding to each search result repeatedly. In this process,ranking of respective search results in the search engine result pagestay the same.

FIG. 8 illustrates a schematic diagram of the web search methodaccording to an embodiment of the present disclosure. As shown in FIG. 8, the web search method in an embodiment of the present disclosure doesnot need hand-based operations, and after the character string isinputted, the landing page that is most likely to meet the informationneeds of the user directly may be displayed. After the landing page isclosed, in a process of displaying the search engine result page, thesearch system may not only adjust ranking of the search results in thesearch engine result page according to a degree of user satisfaction inresponse to the landing page, but also detect the user's feedbackinformation (including the user's preference for each search result) inresponse to the search engine result page in real time, to performpersonalized re-ranking on the search results in the search engineresult page.

From the above, in embodiments of the present disclosure, a three-stageinteraction method on the basis of query formulation, landing pageexamination, and SERP examination, which is capable of implementingquery input and search engine control based on the EEG information andeye movement signals. During the interaction, the system may collect theuser's EEG signals in real time, and decode the relevant signals toacquire the user's feedback, thereby improving the search experiencethrough query recommendations, re-ranking of search results and thelike.

In the process of query formulation, query recommendations may beprovided for the user based on the inputted character string andtogether with current hot topics on the Internet, reducing a differencebetween the user intention and the inputted text effectively, andproviding certain fault-tolerant capacity.

When the search results are ranked according to the query termssubmitted by the user, the EEG information of the user generated whenformulating the queries may be used as features, which may include theinformation needs of the user, a search context and other sufficientuser-side information, thereby providing valuable feature input for theranking of the search engine results. Moreover, during the interactionof re-ranked SERP examination, the EEG information may be decoded inreal time to acquire a degree of satisfaction, emotion state and otherhigh-grade cognition activities of the user, and the ranking strategy ofthe search engine is adjusted dynamically according to the cognitionfeedbacks of the user, thereby meeting the information needs of the usereffectively and efficiently.

Therefore, the web search method in the embodiments of the presentdisclosure may help the user to interact with the search engine (such asinputting a query, controlling a search, and the like) withouthand-based operations through an interactive mode based on mainly theEEG information and in combination with the eye movement information,such that the information needs and feedback of the user are betterunderstood, thereby meeting the information needs more efficiently. Andit is helpful for the physically challenged who cannot use the keyboardor mouse, and the healthy people in special scenarios to perform websearch and browsing normally. Embodiments of the present disclosureprovide a theoretical paradigm for constructing a search engine based ona brain-computer interface, which has great market prospect with themarketization of EEG equipment.

It may be understood that the above method embodiments described in thepresent disclosure may be combined with each other to form differentembodiments without departing from principles and logics, which are notrepeated in the present disclosure for concise illustration. It may beappreciated by those skilled in the art that a specific executionsequence of various steps in the above method of specificimplementations are determined on the basis of their functions andpossible intrinsic logics.

Furthermore, the present disclosure further provides an apparatus, anelectronic device, a computer-readable storage medium and a program forweb search, all of which may be configured to implement any web searchmethod provided by the present disclosure. For the correspondingtechnical solutions and descriptions, refer to the corresponding recordsin the method section, which will not be repeated here.

FIG. 9 illustrates a block diagram of a web search apparatus accordingto an embodiment of the present disclosure. The apparatus is applied toa user interface. As shown in FIG. 9 , the apparatus includes:

a first displaying module 91 configured to display a visual spellingpage, where the visual spelling page includes a query inputtingkeyboard;

a first acquiring module 92 configured to acquire a steady-state visualevoked potential in EEG information, where the steady-state visualevoked potential is generated when a user gazes at keys on the queryinputting keyboard;

a first sending module 93 configured to send the steady-state visualevoked potential to a server, such that the server, based on thesteady-state visual evoked potential, determines a character stringinputted by a user and a landing page corresponding to the characterstring; and

a second displaying module 94 configured to display the landing page inresponse to receiving the landing page sent by the server.

In a possible implementation, the apparatus further includes: a secondacquiring module configured to acquire EEG information after the landingpage is displayed, where the EEG information is generated when the usergazes at the landing page; a second sending module configured to sendthe EEG information to the server, such that the server detects theuser's feedback information in response to the landing page based on theEEG information, and determines a search engine result page based on thefeedback information, where the feedback information includessatisfaction and emotion information determined based on the EEGinformation, and the search engine result page includes at least twosearch results corresponding to the character string; and a thirddisplaying module configured to display the search engine result page inresponse to receiving the search engine result page sent by the server.

In a possible implementation, the visual spelling page also includes aquery suggestion module, and the first sending module 93 is configuredto: send the steady-state visual evoked potential to the server, suchthat the server, based on the steady-state visual evoked potential,determines the character string inputted by the user and at least onequery term corresponding to the character string; display the characterstring and the at least one query term through the query suggestionmodule in response to receiving the character string and the at leastone query term sent by the server; acquire a search context in the EEGinformation, where the search context includes user state information;and send the search context to the server, such that the serverdetermines the landing page based on the at least one query term and thesearch context.

In a possible implementation, the apparatus is further configured to:acquire eye movement information, and the first sending module 93 isconfigured to send the steady-state visual evoked potential and the eyemovement information to the server, such that the server determines thecharacter string inputted by the user based on the steady-state visualevoked potential and the eye movement information.

In a possible implementation, the apparatus is further configured to: ina case where the eye movement information and/or the EEG informationindicates paying attention to a preset area in the landing page displaya landing keyboard in the landing page, where the landing keyboardincludes at least one key position, and each key position corresponds toa different operation; and determine a selected key position accordingto the eye movement information and/or the EEG information, and executean operation corresponding to the key position.

In a possible implementation, the apparatus is further configured to: ina case where the eye movement information and/or the EEG informationindicates paying attention to a preset area in the search engine resultpage, display at least one search engine result keyboard, where eachsearch engine result keyboard includes at least one key position, andeach key position corresponds to a different operation; and determine aselected key position based on the eye movement information and/or theEEG information, and execute an operation corresponding to the keyposition.

FIG. 10 illustrates a block diagram of a web search apparatus accordingto an embodiment of the present disclosure. The apparatus is applied toa server. As shown in FIG. 10 , the apparatus includes:

a first receiving module 101 configured to receive a steady-state visualevoked potential sent by a user interface, where the steady-state visualevoked potential is generated when the user gazes at keys on a queryinputting keyboard of the user interface;

a first determining module 102 configured to, based on the steady-statevisual evoked potential, determine a character string inputted by theuser and a landing page corresponding to the character string; and

a third sending module 103 configured to send the landing page to theuser interface, such that the user interface displays the landing page.

In a possible implementation, the apparatus further includes: a secondreceiving module configured to receive EEG information sent by the userinterface, where the EEG information is generated when the user gazes atthe landing page of the user interface; a detecting module configured todetect the user's feedback information in response to the landing pagebased on the EEG information, where the feedback information includesemotion information determined based on the EEG information; a seconddetermining module configured to determine a search engine result pagebased on the feedback information, where the search engine result pageincludes at least two search results corresponding to the characterstring; and a fourth sending module configured to send the search engineresult page to the user interface, such that the user interface displaysthe search engine result page.

In a possible implementation, the first determining module 102 isconfigured to: based on the steady-state visual evoked potential,determine the character string inputted by the user and at least onequery term corresponding to the character string; send the characterstring and the at least one query term corresponding to the characterstring to the user interface, such that the user interface displays thecharacter string and the at least one query term in a query suggestionmodule of a visual spelling page; receive a search context sent by theuser interface, where the search context includes user stateinformation; and determine the landing page based on the at least onequery term and the search context.

In a possible implementation, the second determining module isconfigured to: determine a difference between a subject of each searchresult and a subject of each of the landing page; in a case where thefeedback information is dissatisfaction, rank the search results with alarger difference higher than the search results with a smallerdifference in the search engine result page, or in a case where thefeedback information is satisfaction, rank the search results with alarger difference lower than the search results with a smallerdifference in the search engine result page.

In a possible implementation, the apparatus is further configured to:receive the EEG information sent by the user interface in real time,where the EEG information is generated when the user browses the searchengine result page displayed by the user interface; detect the user'spreference information about the search results in the search engineresult page in real time based on the acquired EEG information; re-rankrespective search results in the search engine result page in real time;and send the re-ranked search engine result page to the user interfacein real time, and the user interface displays the re-ranked searchengine result page in real time.

In a possible implementation, the based on the steady-state visualevoked potential, determining the character string inputted by the userand the at least one query term corresponding to the character string,includes: determining the character string inputted by the user based onthe steady-state visual evoked potential; and determining at least onequery term corresponding to the character string based on a candidateword generation algorithm with massive information on the Internet.

In a possible implementation, the apparatus is further configured to:receive eye movement information sent by the user interface, and thefirst determining module 102 is configured to determine the characterstring inputted by the user based on the steady-state visual evokedpotential and the eye movement information.

In a possible implementation, the detecting module is configured to:input the EEG information to a satisfaction predicting module todetermine a degree of user satisfaction; determine a correspondingrelationship between the degree of user satisfaction and each textcontent in the landing page based on the eye movement information and/orthe EEG information; and determine the feedback information based on thedegree of user satisfaction corresponding to each text content in thelanding page.

In some embodiments, functions or modules of the apparatus provided inthe embodiments of the present disclosure may be configured to executethe method described in the above method embodiments, through themethods described in the above descriptions of the method embodiments,which are not repeated here for brevity.

An embodiment of the present disclosure further provides acomputer-readable storage medium having computer program instructionsstored thereon, where the computer program instructions, when executedby a processor, implement the above method. The computer-readablestorage medium may be a volatile or a non-volatile computer-readablestorage medium.

An embodiment of the present disclosure further provides an electronicdevice, which includes a processor and a memory configured to storeprocessor-executable instructions, where the processor is configured tocall the instructions stored in the memory to execute the above method.

An embodiment of the present disclosure further provides a computerprogram, including computer-readable codes, or a nonvolatilecomputer-readable storage medium carrying the computer-readable codes,where when the computer-readable codes are executed in a processor of anelectronic device, the processor in the electronic device executes themethod.

The electronic device may be embodied as a terminal, a server or adevice in any other form.

FIG. 11 illustrates a block diagram of an electronic device 800according to an embodiment of the present disclosure. For example, theelectronic device 800 may be a mobile phone, a computer, a digitalbroadcast terminal, a message transceiver, a game console, a tabletdevice, a medical device, a fitness device, a personal digital assistantor any other terminal.

Referring to FIG. 11 , the electronic device 800 may include one or moreof the following components: a processing component 802, a memory 804, apower supply component 806, a multimedia component 808, an audiocomponent 810, an input/output (I/O) interface 812, a sensor component814 and a communication component 816.

The processing component 802 generally controls the overall operation ofthe electronic device 800, such as operations related to display, phonecall, data communication, camera operation and record operation. Theprocessing component 802 may include one or more processors 820 toexecute instructions, to complete all or some steps of the above method.Furthermore, the processing component 802 may include one or moremodules for interaction between the processing component 802 and othercomponents. For example, the processing component 802 may include amultimedia module to facilitate interaction between the multimediacomponent 808 and the processing component 802.

The memory 804 is configured to store various types of data to supportoperations of the electronic device 800. Examples of the data includeinstructions for any application or method operated on the electronicdevice 800, contact data, telephone directory data, messages, pictures,videos, and the like. The memory 804 may be any type of volatile ornon-volatile storage devices or a combination thereof, such as StaticRandom Access Memory (SRAM), Electronic Erasable Programmable Read-OnlyMemory (EEPROM), Erasable Programmable Read-Only Memory (EPROM),Read-Only Memory (ROM), a magnetic memory, a flash memory, a magneticdisk or a compact disk.

The power supply component 806 supplies electric power to components ofthe electronic device 800. The power supply component 806 may include apower supply management system, one or more power supplies, and othercomponents related to power generation, management and allocation of theelectronic device 800.

The multimedia component 808 includes a screen providing an outputinterface between the electronic device 800 and a user. In someembodiments, the screen may include a Liquid Crystal Display (LCD) and aTouch Panel (TP). If the screen includes the touch panel, the screen maybe embodied as a touch screen to receive an input signal from the user.The touch panel includes one or more touch sensors to sense the touch,sliding, and gestures on the touch panel. The touch sensor may not onlysense a boundary of the touch or sliding action, but also detectduration and pressure related to the touch or sliding operation. In someembodiments, the multimedia component 808 includes a front camera and/ora rear camera. When the electronic device 800 is in an operating modesuch as a shooting mode or a video mode, the front camera and/or therear camera may receive external multimedia data. Each front camera andrear camera may be a fixed optical lens system or have a focal lengthand optical zooming capability.

The audio component 810 is configured to output and/or input an audiosignal. For example, the audio component 810 includes a microphone(MIC). When the electronic device 800 is in the operating mode such as acall mode, a record mode and a voice identification mode, the microphoneis configured to receive the external audio signal. The received audiosignal may be further stored in the memory 804 or sent by thecommunication component 816. In some embodiments, the audio component810 also includes a loudspeaker configured to output the audio signal.

The I/O interface 812 provides an interface between the processingcomponent 802 and a peripheral interface module. The peripheralinterface module may be a keyboard, a click wheel, a button, or thelike. The button may include but is not limited to a home button, avolume button, a start button and a lock button.

The sensor component 814 includes one or more sensors configured toprovide state evaluation in various aspects for the electronic device800. For example, the sensor component 814 may detect an on/off state ofthe electronic device 800 and relative positions of the components suchas a display and a small keyboard of the electronic device 800. Thesensor component 814 may also detect the position change of theelectronic device 800 or a component of the electronic device 800,presence or absence of a user contact with electronic device 800,orientation or acceleration/deceleration of the electronic device 800and the temperature change of the electronic device 800. The sensorcomponent 814 may include a proximity sensor configured to detect thepresence of nearby objects without any physical contact. The sensorcomponent 814 may further include an optical sensor such as aComplementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device(CCD) image sensor applied in an imaging application. In someembodiments, the sensor component 814 may further include anacceleration sensor, a gyroscope sensor, a magnetic sensor, a pressuresensor or a temperature sensor.

The communication component 816 is configured to facilitatecommunication in a wire or wireless manner between the electronic device800 and other devices. The electronic device 800 may access a wirelessnetwork based on a communication standard, such as Wireless Fidelity(Wi-Fi), second generation mobile communication technology (2G), thirdgeneration mobile communication technology (3G), fourth generationmobile communication (4G) technology/long-term evolution of universalmobile communication technology (LTE), fifth generation mobilecommunication technology (5G) or a combination thereof. In an exemplaryembodiment, the communication component 816 receives a broadcast signalor broadcast related information from an external broadcast managementsystem via a broadcast channel. In an exemplary embodiment, thecommunication component 816 further includes a Near Field Communication(NFC) module to promote short range communication. For example, the NFCmodule may be implemented on the basis of Radio Frequency Identification(RFID) technology, Infrared Data Association (IrDA) technology,Ultra-Wide Band (UWB) technology, Bluetooth (BT) technology and othertechnologies.

In exemplary embodiments, the electronic device 800 may be implementedby one or more Application Specific Integrated Circuits (ASIC), DigitalSignal Processors (DSP), Digital Signal Processing Device (DSPD),Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA),controllers, microcontrollers, microprocessors or other electronicelements to execute the above method.

In an exemplary embodiment, there is further provided a non-volatilecomputer-readable storage medium, such as a memory 804 includingcomputer program instructions. The computer program instructions may beexecuted by a processor 820 of an electronic device 800 to implement theabove method.

The present disclosure relates to the field of augmented reality. Byobtaining the image information of a target object in the realenvironment, and then detecting or identifying the related features,states and attributes of the target object with the help of variousvisual correlation algorithms, the AR effect of combining virtual andreality matching with specific applications may be obtained. Forexample, the target object may relate to faces, limbs, gestures,actions, and the like related to human bodies, or markers and markersrelated to objects, or sand tables, display areas or display itemsrelated to venues or places, and the like. The related algorithms mayinvolve visual positioning, SLAM, 3D reconstruction, image registration,background segmentation, key point extraction and tracking of objects,pose or depth detection of objects, and the like. The application mayinvolve not only interactive scenes such as tutorials, navigation,explanation, reconstruction, and virtual effect overlay display relatedto real scenes or objects, but also interactive scenes related topeople, such as makeup beautification, body beautification, specialeffect display, and virtual model display. Convolutional neural networkmay be used for detecting or identifying the relevant features, statesand attributes of the target object. The convolutional neural network isthe neural model obtained by model training based on a deep learningframework.

FIG. 12 illustrates a block diagram of an electronic device 1900according to an embodiment of the present disclosure. For example, theelectronic device 1900 may be provided as a server. Referring to FIG. 12, the electronic device 1900 includes a processing component 1922, andfurther includes one or more processors and memory resources representedby a memory 1932, which are configured to store instructions executed bythe processing component 1922, such as an application program. Theapplication program stored in the memory 1932 may include one or moremodules each corresponding to a group of instructions. Furthermore, theprocessing component 1922 is configured to execute the instructions toexecute the above method.

The electronic device 1900 may further include a power supply component1926 configured to perform power supply management on the electronicdevice 1900, a wire or wireless network interface 1950 configured toconnect the electronic device 1900 to a network, and an Input/output(I/O) interface 1958. The electronic device 1900 may run an operatingsystem stored in the memory 1932, such as windows server operatingsystem (Windows Server™), graphical user interface operating system (MacOS X™) introduced by Apple, a multi-user and multi-process computeroperating system (Unix™), Unix-like operating system with free and opensource codes (Linux™), open source Unix-like operating system (FreeBSD™)or the like.

In an exemplary embodiment, there is further provided a non-volatilecomputer-readable storage medium, such as a memory 1932 includingcomputer program instructions. The computer program instructions may beexecuted by a processing module 1922 of an electronic device 1900 toexecute the above method.

The present disclosure may be implemented by a system, a method, and/ora computer program product. The computer program product may include acomputer-readable storage medium having computer-readable programinstructions for causing a processor to carry out the aspects of thepresent disclosure stored thereon.

The computer-readable storage medium may be a tangible device that mayretain and store instructions used by an instruction executing device.The computer-readable storage medium may be, but not limited to,electronic storage device, magnetic storage device, optical storagedevice, electromagnetic storage device, semiconductor storage device, orany proper combination thereof. A non-exhaustive list of more specificexamples of the computer-readable storage medium includes: portablecomputer diskette, hard disk, Random Access Memory (RAM), Read-OnlyMemory (ROM), Erasable Programmable Read-Only Memory (EPROM or Flashmemory), Static Random Access Memory (SRAM), portable Compact DiscRead-Only Memory (CD-ROM), Digital Versatile Disk (DVD), memory stick,floppy disk, mechanically encoded device (for example, punch-cards orraised structures in a groove having instructions recorded thereon), andany proper combination thereof. A computer-readable storage mediumreferred herein should not to be construed as transitory signal per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signal transmitted through a wire.

Computer-readable program instructions described herein may bedownloaded to individual computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via network, for example, the Internet, local regionnetwork, wide region network and/or wireless network. The network mayinclude copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer-readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium in therespective computing/processing devices.

Computer-readable program instructions for carrying out the operation ofthe present disclosure may be assembler instructions,Instruction-Set-Architecture (ISA) instructions, machine instructions,machine-related instructions, microcode, firmware instructions,state-setting data, or source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language, such as Smalltalk, C++ or the like, andthe conventional procedural programming languages, such as the “C”programming language or similar programming languages. Thecomputer-readable program instructions may be executed completely on theuser computer, partly on the user computer, as a stand-alone softwarepackage, partly on the user computer and partly on a remote computer, orcompletely on a remote computer or a server. In the scenario with remotecomputer, the remote computer may be connected to the user computerthrough any type of network, including Local Region Network (LAN) orWide Region Network (WAN), or connected to an external computer (forexample, through the Internet connection from an Internet ServiceProvider). In some embodiments, electronic circuitry, such asprogrammable logic circuitry, Field-Programmable Gate Arrays (FPGA), orProgrammable Logic Arrays (PLA), may be customized from stateinformation of the computer-readable program instructions; and theelectronic circuitry may execute the computer-readable programinstructions, to achieve the aspects of the present disclosure.

Aspects of the present disclosure have been described herein withreference to the flowchart and/or the block diagrams of the method,device (system), and computer program product according to theembodiments of the present disclosure. It will be appreciated that eachblock in the flowchart and/or the block diagram, and combinations ofblocks in the flowchart and/or block diagram, may be implemented by thecomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a general purpose computer, a dedicated computer, or otherprogrammable data processing devices, to produce a machine, such thatthe instructions create means for implementing the functions/actsspecified in one or more blocks in the flowchart and/or block diagramwhen executed by the processor of the computer or other programmabledata processing devices. These computer-readable program instructionsmay also be stored in a computer-readable storage medium, where theinstructions cause a computer, a programmable data processing deviceand/or other devices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereinincludes a product that includes instructions implementing aspects ofthe functions/acts specified in one or more blocks in the flowchartand/or block diagram.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing devices, or other devicesto have a series of operational steps performed on the computer, otherprogrammable devices or other devices, to produce a computer implementedprocess, such that the instructions executed on the computer, otherprogrammable devices or other devices implement the functions/actsspecified in one or more blocks in the flowchart and/or block diagram.

The flowcharts and block diagrams in the drawings illustrate thearchitecture, function, and operation that may be implemented by thesystem, method and computer program product according to the variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagram may represent a part of a module, a programsegment, or a part of code, which includes one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions denoted in the blocks mayoccur in an order different from that denoted in the drawings. Forexample, two contiguous blocks may, in fact, be executed substantiallyconcurrently, or sometimes they may be executed in a reverse order,depending upon the functions involved. It will also be noted that eachblock in the block diagram and/or flowchart, and combinations of blocksin the block diagram and/or flowchart, may be implemented by dedicatedhardware-based systems performing the specified functions or acts, or bycombinations of dedicated hardware and computer instructions.

The computer program product may be implemented specifically byhardware, software or a combination thereof. In an optional embodiment,the computer program product is specifically embodied as a computerstorage medium. In another optional embodiment, the computer programproduct is specifically embodied as a software product, such as SoftwareDevelopment Kit (SDK) and the like.

Although the embodiments of the present disclosure have been describedabove, it will be appreciated that the above descriptions are merelyexemplary, but not exhaustive; and that the disclosed embodiments arenot limiting. A number of variations and modifications may occur tothose skilled in the art without departing from the scopes and spiritsof the described embodiments. The terms in the present disclosure areselected to provide the best explanation on the principles and actualapplications of the embodiments or the technical improvements to thearts on market, or to make the embodiments described hereinunderstandable to those skilled in the art.

What is claimed is:
 1. A method for web search, applied to a userterminal, the method comprising: displaying a visual spelling page,wherein the visual spelling page comprises a query keyboard; acquiring asteady-state visual evoked potential in electroencephalogram (EEG)information, wherein the steady-state visual evoked potential isgenerated when a user gazes at a key on the query keyboard; sending thesteady-state visual evoked potential to a server, such that the server,based on the steady-state visual evoked potential, determines acharacter string inputted by the user and a landing page correspondingto the character string; and in response to receiving the landing pagesent by the server, displaying the landing page.
 2. The method accordingto claim 1, further comprising: after the landing page is displayed,acquiring EEG information, wherein the EEG information is generated whenthe user gazes at the landing page; sending the EEG information to theserver, such that the server detects the user's feedback information inresponse to the landing page based on the EEG information and determinesa search engine result page based on the feedback information, whereinthe feedback information comprises emotion information determined basedon the EEG information, and the search engine result page comprises atleast two search results corresponding to the character string; and inresponse to receiving the search engine result page sent by the server,displaying the search engine result page.
 3. The method according toclaim 1, wherein the visual spelling page further comprises a querysuggestion module, the sending the steady-state visual evoked potentialto a server, such that the server, based on the steady-state visualevoked potential, determines a character string inputted by the user anda landing page corresponding to the character string, comprises: sendingthe steady-state visual evoked potential to the server, such that theserver, based on the steady-state visual evoked potential, determinesthe character string inputted by the user and at least one query termcorresponding to the character string; in response to receiving thecharacter string and the at least one query term sent by the server,displaying the character string and the at least one query term throughthe query suggestion module; causing the server to acquire a searchcontext in the EEG information, wherein the search context comprisesuser state information; and sending a search context from the userterminal to the server, such that the server determines the landing pagebased on the at least one query term and the search context.
 4. Themethod according to claim 1, further comprising: acquiring eye movementinformation, and the sending the steady-state visual evoked potential tothe server, such that the server determines the character stringinputted by the user based on the steady-state visual evoked potential,comprises: sending the steady-state visual evoked potential and the eyemovement information to the server, such that the server determines thecharacter string inputted by the user based on the steady-state visualevoked potential and the eye movement information.
 5. The methodaccording to claim 4, further comprising: based on that the eye movementinformation and/or the EEG information indicates paying attention to apreset area in the landing page, displaying a landing keyboard in thelanding page, wherein the landing keyboard comprises at least one keyposition, and each key position corresponds to a different operation;and determining a selected key position based on the eye movementinformation and/or the EEG information, and executing an operationcorresponding to the key position.
 6. The method according to claim 4,further comprising: based on that the eye movement information and/orthe EEG information indicates paying attention to a preset area in thesearch engine result page, displaying at least one search engine resultkeyboard, wherein each search engine result keyboard comprises at leastone key position, and each key position corresponds to a differentoperation; and determining a selected key position based on the eyemovement information and/or the EEG information, and executing anoperation corresponding to the key position.
 7. A method for web search,applied to a server, the method comprising: receiving a steady-statevisual evoked potential sent by a user terminal, wherein thesteady-state visual evoked potential is generated when a user gazes at akey on a query keyboard of the user terminal; based on the steady-statevisual evoked potential, determining a character string inputted by theuser and a landing page corresponding to the character string; andsending the landing page to the user terminal, such that the userterminal displays the landing page.
 8. The method according to claim 7,further comprising: receiving EEG information sent by the user terminal,wherein the EEG information is generated when the user gazes at thelanding page of the user terminal; detecting the user's feedbackinformation in response to the landing page based on the EEGinformation, wherein the feedback information comprises emotioninformation determined based on the EEG information; determining asearch engine result page based on the feedback information, wherein thesearch engine result page comprises at least two search resultscorresponding to the character string; and sending the search engineresult page to the user terminal, such that the user terminal displaysthe search engine result page.
 9. The method according to claim 7,wherein the based on the steady-state visual evoked potential,determining a character string inputted by the user and a landing pagecorresponding to the character string, comprises: based on thesteady-state visual evoked potential, determining the character stringinputted by the user and at least one query term corresponding to thecharacter string; sending the character string and the at least onequery term corresponding to the character string to the user terminal,such that the user terminal displays the character string and the atleast one query term in a query suggestion module of a visual spellingpage; receiving a search context sent by the user terminal, wherein thesearch context comprises user state information; and determining thelanding page based on the at least one query term and the searchcontext.
 10. The method according to claim 8, wherein the determining asearch engine result page based on the feedback information, comprises:determining a difference between a subject of each search result and asubject of each of the landing page; based on the feedback informationbeing dissatisfaction, ranking a search result with a larger differencehigher than a search result with a smaller difference in the searchengine result page, or based on the feedback information beingsatisfaction, ranking a search result with a larger difference lowerthan a search result with a smaller difference in the search engineresult page.
 11. The method according to claim 7, further comprising:receiving the EEG information sent by the user terminal in real time,wherein the EEG information is generated when the user browses thesearch engine result page displayed by the user terminal; detecting theuser's preference information about the search results in the searchengine result page in real time based on the acquired EEG information;re-ranking respective search results in the search engine result page inreal time based on the preference information; and sending the re-rankedsearch engine result page to the user terminal, such that the userterminal displays the re-ranked search engine result page in real time.12. The method according to claim 9, wherein the based on thesteady-state visual evoked potential, determining the character stringinputted by the user and at least one query term corresponding to thecharacter string, comprises: determining the character string inputtedby the user based on the steady-state visual evoked potential; anddetermining at least one query term corresponding to the characterstring by means of a candidate word generation algorithm with massiveinformation on Internet.
 13. The method according to claim 7, furthercomprising: receiving eye movement information sent by the userterminal, and the determining the character string inputted by the userbased on the steady-state visual evoked potential, comprises:determining the character string inputted by the user based on thesteady-state visual evoked potential and the eye movement information.14. The method according to claim 8, wherein the detecting the user'sfeedback information in response to the landing page based on the EEGinformation comprises: inputting the EEG information to a satisfactionpredicting module to determine a degree of satisfaction of the user;determining a corresponding relationship between the degree ofsatisfaction and each text content in the landing page based on the eyemovement information and/or the EEG information; and determining thefeedback information based on the degree of satisfaction correspondingto each text content in the landing page.
 15. An electronic device,comprising: a processor; and a memory configured to storeprocessor-executable instructions, wherein the processor is configuredto call the instructions stored in the memory to execute a method forweb search, applied to a user terminal, the method comprising:displaying a visual spelling page, wherein the visual spelling pagecomprises a query keyboard; acquiring a steady-state visual evokedpotential in EEG information, wherein the steady-state visual evokedpotential is generated when a user gazes at a key on the query keyboard;sending the steady-state visual evoked potential to a server, theserver, based on the steady-state visual evoked potential, a characterstring inputted by the user and a landing page corresponding to thecharacter string; and in response to receiving the landing page sent bythe server, displaying the landing page.
 16. A computer-readable storagemedium having computer program instructions stored thereon, wherein thecomputer program instructions, when executed by a processor, implement amethod for web search, applied to a user terminal, the methodcomprising: displaying a visual spelling page, wherein the visualspelling page comprises a query keyboard; acquiring a steady-statevisual evoked potential in EEG information, wherein the steady-statevisual evoked potential is generated when a user gazes at a key on thequery keyboard; sending the steady-state visual evoked potential to aserver, the server, based on the steady-state visual evoked potential, acharacter string inputted by the user and a landing page correspondingto the character string; and in response to receiving the landing pagesent by the server, displaying the landing page.